Sentiment analysis of political tweets

This application displays the sentiment extracted from tweets targeted at each of the leading US 2016 elections candidates.
The sentiments are classified as "Negative", "Neutral", or "Positive". A separate training data set was used for giving labels to live tweets extracted
for each of the candidates. Please go to "About this data set" tab for further information on the data used.

Prediction methodology

Using the labeled training data from kaggle
GOP twitter sentiment data , I built a multi-class model using Naive Bayes classification model. Each class corresponds to the three
possible emotions that are detected from tweets - positive/neutral/negative. I then used this trained model on live tweets targeted at each
of the leading US 2016 elections candidates to extract emotion from them.

Analysis of bar chart

The stacked bar chart displayed above shows the percentages of
negative/neutral/positive emotions detected in tweets targeted at US 2016 election candidates.
From this graph, we can see that maximum percentage of positive tweets were targeted at Marco Rubio whereas
maximum percentage of negative tweets were targeted at Ted Cruz.